Sensitivity Analysis in Yield Optimization of MEMS Tunable Capacitors
Bibliographic record
Abstract
The fabrication of MEMS tunable capacitors faces many uncertainties in which the fabricated dimensions differ from nominal values. This deviation in a tunable capacitor may cause significant variation in the capacitance-voltage response. In this paper, the effect of uncertainty in parallel-plate tunable capacitors is studied to maximize the yield under given criteria. A new method for yield optimization of tunable MEMS capacitors is developed. The method can take into account any arbitrary distribution and is not restricted to normality assumptions. The optimal designs verified by Monte-Carlo simulation exhibits considerable improvement in the yield. A sensitivity analysis is then performed to refine the design variables and maximize the yield based on the most effective parameters. When the fabrication process is already established and cannot be changed, the method can be employed to estimate the final yield for the process. The advantage of this method is demonstrated by numerical examples where the yield using initial design parameters is compared to the yield of the device with optimum parameters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".